Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018): Difference between revisions

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{{Announcement|Note: The room for this course has changed to the bigger room 0.101!)}}
{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}
== Details ==
== Details ==
{{CourseDetails
{{CourseDetails
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|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|ta=
|time=Thursday, 14-16 (tentative)
|time=Thursday, 14-16  
|place=Ifi 3.101 (tentative)
|place=Ifi 0.101  
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}
}}
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==Prerequisites==
==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of Python or R...
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries
*...and relevant libraries (e.g., SciKit Learn, Pandas, caret, etc.)


==Preliminary Schedule==
==Schedule==
{| {{Prettytable|width=}}
{| {{Prettytable|width=}}
|-
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
|-
| align="right" | 19.10.2017
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
|
|-
|-
| align="right" | 26.10.2017
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
| Lecture 2: The Python Data Science Stack
|
|-
|-
| align="right" | 02.11.2017
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
| Task 1: Intermediate meeting
|
|-
|-
| align="right" | 09.11.2017
| align="right" | 09.11.2017
| No lecture
| No lecture
|
|-
|-
| align="right" | 16.11.2017
| align="right" | 16.11.2017
| No lecture
| Task 1: Presentation of Exemplary Solution // Task 2: Release
|
|-
|-
| align="right" | 23.11.2017
| align="right" | 23.11.2017
| Task 1: Presentations // Task 2: Release
| Lecture 3: Advanced Algorithms for Data Science
|
|-
|-
| align="right" | 30.11.2017
| align="right" | 30.11.2017
| Lecture 3: Advanced Algorithms for Data Science
| Lecture 4: Evaluation and Tuning of Models
|
|-
|-
| align="right" | 07.12.2017
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
| Task 2: Intermediate meeting
|
|-
|-
| align="right" | 14.12.2017
| align="right" | 14.12.2017
| Lecture 4: Evaluation and Tuning of Models
| No lecture
|
|-
|-
| align="right" | 21.12.2017
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
| Task 2: Presentations // Task 3: Release
|
|-
|-
| align="right" | 04.01.2018
| align="right" | 04.01.2018
| No lecture
| No lecture
|
|-
|-
| align="right" | 11.01.2018
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
| Task 3: Intermediate meeting I
|
|-
|-
| align="right" | 18.01.2018
| align="right" | 18.01.2018
| No lecture  
| No lecture  
|
|-
|-
| align="right" | 25.01.2018
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
| Task 3: Intermediate meeting II
| 
|-
|-
| align="right" | 01.02.2018
| align="right" | 01.02.2018
| No lecture
| No lecture  
| 
|-
|-
| align="right" | 08.02.2018-22.02.2018
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
| Task 3: Presentations  
| 
|-
|-
| align="right" | 31.03.2018
| align="right" | 31.03.2018
| Final Report deadline  
| Final Report deadline  
|
|-
|-
|}
|}

Latest revision as of 15:26, 15 March 2018

Imbox content.png Note: The room for this course has changed to the bigger room 0.101!)


Imbox content.png Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.


Details

Workload/ECTS Credits: 180h, 6 ECTS
Module: M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
Teaching assistant:
Time: Thursday, 14-16
Place: Ifi 0.101
UniVZ [1]


Course Organization

In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

  • Introduction to the practical machine learning pipeline
  • Exploratory data analysis
  • The Python Data Science stack
  • How to deal with unbalanced data
  • Advanced algorithms for Data Science (an overview of competition winning algorithms)
  • Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester. Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

Prerequisites

  • You are highly recommended to have completed a course on Data Science (e.g., "Data Science and Big Data Analytics" taught by Dr. Steffen Herbold or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
  • Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

Schedule

When? What?
19.10.2017 Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
26.10.2017 Lecture 2: The Python Data Science Stack
02.11.2017 Task 1: Intermediate meeting
09.11.2017 No lecture
16.11.2017 Task 1: Presentation of Exemplary Solution // Task 2: Release
23.11.2017 Lecture 3: Advanced Algorithms for Data Science
30.11.2017 Lecture 4: Evaluation and Tuning of Models
07.12.2017 Task 2: Intermediate meeting
14.12.2017 No lecture
21.12.2017 Task 2: Presentations // Task 3: Release
04.01.2018 No lecture
11.01.2018 Task 3: Intermediate meeting I
18.01.2018 No lecture
25.01.2018 Task 3: Intermediate meeting II
01.02.2018 No lecture
08.02.2018-22.02.2018 Task 3: Presentations
31.03.2018 Final Report deadline
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